Zhou Ju, Liu Xinyu, Liao Qianghua, Wang Tao, Wang Lin, Yang Pin
Tech X Academy, Shenzhen Polytechnic University, Shenzhen 518055, China.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Sensors (Basel). 2025 Aug 6;25(15):4847. doi: 10.3390/s25154847.
In tool wear monitoring, the efficient fusion of multi-source sensor signals poses significant challenges due to their inherent heterogeneous characteristics. In this paper, we propose a Multi-Sensor Multi-Domain feature fusion Transformer (MSMDT) model that achieves precise tool wear prediction through innovative feature engineering and cross-modal self-attention mechanisms. Specifically, we first develop a physics-aware feature extraction framework, where time-domain statistical features, frequency-domain energy features, and wavelet packet time-frequency features are systematically extracted for each sensor type. This approach constructs a unified feature matrix that effectively integrates the complementary characteristics of heterogeneous signals while preserving discriminative tool wear signatures. Then, a position-embedding-free Transformer architecture is constructed, which enables adaptive cross-domain feature fusion through joint global context modeling and local feature interaction analysis to predict tool wear values. Experimental results on the PHM2010 demonstrate the superior performance of MSMDT, outperforming state-of-the-art methods in prediction accuracy.
在刀具磨损监测中，由于多源传感器信号固有的异构特性，其有效融合面临重大挑战。在本文中，我们提出了一种多传感器多域特征融合Transformer（MSMDT）模型，该模型通过创新的特征工程和跨模态自注意力机制实现精确的刀具磨损预测。具体而言，我们首先开发了一个物理感知特征提取框架，针对每种传感器类型系统地提取时域统计特征、频域能量特征和小波包时频特征。这种方法构建了一个统一的特征矩阵，有效整合了异构信号的互补特性，同时保留了有区分性的刀具磨损特征。然后，构建了一种无位置嵌入的Transformer架构，通过联合全局上下文建模和局部特征交互分析实现自适应跨域特征融合，以预测刀具磨损值。在PHM2010上的实验结果证明了MSMDT的卓越性能，在预测精度上优于现有方法。
